Abstract The National Institute of Mental Health (NIMH) Data Archive (NDA) collects and shares de-identified human subjects data from hundreds of NIH-funded research projects across many mental health-related scientific domains with qualified researchers. A new NDA repository is created for the National Institute on Alcohol Abuse and Alcoholism Data Archive (NIAAADA) and will serve as the portal for NIAAA-related data submissions and access. The NIAAA data-sharing policy (NOT-AA-18-010) released in June of 2018, requires that? beginning in 2019?all NIAAA grant applications involving human subjects must include plans for the submission of study data to NIAAADA. The first wave of data submission to NIAAADA is expected in 2020. Many obstacles exist for alcohol researchers to comply with this policy, especially for those with limited budget and information technology and data management support. To submit the study data, researchers have to map their data to the right fields in the right format in a given data template, which often requires researchers to manipulate their data by-hand or with complicated scripts, or request a new data structure from NDA, which compromises the goal of data sharing. These data submission processes are error prone, time-consuming, and require certain technical skills. Our ultimate goal is to provide a nearly-automated process for the submission of alcohol research data into the NIAAADA so that the data submission can be performed accurately and efficiently by alcohol researchers with minimum IT knowledge and resources. We propose to develop and validate the Share HumAn REsearch (SHARE) platform to address the unmet need for assisting alcohol researchers with the submission of study data to NIAAADA. SHARE does so by offering the SHARE Measure Library of pre-defined measures that are already mapped to NDA data dictionaries. Alcohol researchers need to only select the measures that they want to use for their studies and let SHARE do the rest for data submission to NIAAADA. Phase I aims include: 1) Collect stakeholder feedback from ten alcohol researchers via focus groups and interviews to understand how they currently collect and manage their human subject questionnaire data and explore how they would submit the data to the NIAAADA with an ?ideal? tool; 2) Develop the prototype using a user-centric design process and the latest e-technologies. Also code ten common alcohol measures in the SHARE measure library using the NDA data dictionary standards and integrate the SHARE assessment engine with REDCap, a popular data collection program; 3) Evaluate the prototype by conducting two rounds of usability tests with twenty alcohol researchers to evaluate system usability and perceived usefulness of the SHARE solution. Also use the NDA Validator to validate data exported from SHARE.